Ontology visualization methods—a survey

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  • 1.10 Ontology Visualization MethodsA Survey AKRIVI KATIFORI and CONSTANTIN HALATSIS University of Athensand GEORGE LEPOURAS, COSTAS VASSILAKIS, and EUGENIA GIANNOPOULOU University of PeloponneseOntologies, as sets of concepts and their interrelations in a specic domain, have proven to be a useful tool in the areas of digital libraries, the semantic web, and personalized information management. As a result, there is a growing need for effective ontology visualization for design, management and browsing. There exist several ontology visualization methods and also a number of techniques used in other contexts that could be adapted for ontology representation. The purpose of this article is to present these techniques and categorize their characteristics and features in order to assist method selection and promote future research in the area of ontology visualization. Categories and Subject Descriptors: H.3.3 [Information Search and Retrieval]; H.5.2 [Information Interfaces and Presentation]: User InterfacesGraphical user interfaces (GUI); I.3.6 [Computer Graphics]: Methodology and TechniquesInteraction techniques General Terms: Design Additional Key Words and Phrases: Ontology, visualization method, human-computer interaction ACM Reference Format: Katifori, A., Halatsis, C., Lepouras, G., Vassilakis, C., and Giannopoulou, E. 2007. Ontology visualization methodsA survey. ACM Comput. Surv. 39, 4, Article 10 (October 2007), 43 pages DOI = 10.1145/ 1287620.1287621 http://doi.acm.org/10.1145/1287620.12876211. INTRODUCTIONRecently, the continuing progress in network technologies and data storage has made possible the digitization and dissemination of huge amounts of documents, making it more and more difcult for the user to successfully search and retrieve information This work was supported in part by the Greek Secretariat for Research and Development under the PENED 2003 framework. Authors Addresses: A. Katifori and C. Halatsis, Department of Informatics and Telecommunications, University of Athens, Panepistemioupolos, Llissia, Athens, 157 84, Greece; email: vivi@mm.di.noa.gr; G. Lepouras, C. Vassilakis, and E. Giannopoulou, Department of Computer Science and Technology, University of Peloponnese, End of Karaiskaki, 22100, Tripolis, Greece. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for prot or direct commercial advantage and that copies show this notice on the rst page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specic permission and/or a fee. Permissions may be requested from Publications Dept., ACM, Inc., 2 Penn Plaza, Suite 701, New York, NY 10121-0701, USA, fax +1 (212) 869-0481, or permissions@acm.org. c 2007 ACM 0360-0300/2007/10-ART10 $5.00. DOI 10.1145/1287620.1287621 http://doi.acm.org/10.1145/ 1287620.1287621 ACM Computing Surveys, Vol. 39, No. 4, Article 10, Publication date: October 2007.

2. 10:2A. Katifori et al.both in the Web and in a digital document collection, personal or otherwise. The need for more effective information retrieval has lead to the creation of the semantic web and personalized information management notions, areas of study that take advantage of the semantic context of documents to facilitate their management. In many of the proposed solutions in this eld, it is common to take advantage of an ontology. A term initially borrowed from philosophy, it is now used to denote a set of concepts and their interrelations in a specic domain. Consequently, the need for effective ontology visualization for design, management, and browsing has arisen. Visualization of ontologies is not an easy task. An ontology is something more than a hierarchy of concepts. It is enriched with role relations among concepts and each concept has various attributes related to it. Furthermore, each concept most probably has instances attached to it, which could range from one or two to thousands. Therefore, it is not simple to create a visualization that will effectively display all this information and at the same time allow the user to easily perform various operations on the ontology. In the eld of ontology visualization, there are several works, mostly in 2D. Apart from the systems that propose visualizations especially tailored for ontologies, there are a number of other techniques used in other contexts such as graph or le system visualization, that could be adapted to display ontologies. The purpose of this article is to present these techniques and categorize their characteristics and features in relation with a set of requirements compiled for an ontology visualization tool. Such an overview of techniques may be useful for choosing an ontology visualization for a specic application, taking into account both functional (e.g., navigation capabilities) and nonfunctional (e.g., ontology size) requirements as well as tasks that are related to the specic application. The following sections provide an ontology denition, a detailed description of the techniques, followed by a discussion of their characteristics, and the conclusions. 2. ONTOLOGY DEFINITIONAccording to Gruber [1993], an ontology is an explicit specication of a conceptualization. The term conceptualization is dened as an abstract, simplied view of the world, which needs to be represented for some purpose. It contains the objects, concepts, and other entities that are presumed to exist in some area of interest, and the relations that hold among them. The term ontology is borrowed from philosophy, where an ontology is a systematic account of existence. For knowledge-based systems what exists is exactly that which can be (and has been) represented. Therefore, as dened in Noy and McGuiness [2001], an ontology is a formal explicit description of concepts, or classes in a domain of discourse. Propertiesor slotsof each class describe various features and attributes of the class, and restrictions on slots (called facets or role descriptions) state conditions that must always hold to guarantee the semantic integrity of the ontology. Each slot has a type and could have a restricted number of allowed values. Allowed classes for slots of type Instance are often called a range of a slot. An ontology along with a set of individual instances of classes constitutes a knowledge base. A more mathematical denition can be the following [Amann and Fundulaki 1999]. An ontology is a triple O = (C, S, isa) where: (1) C = {c1 , c2 , . . . , cm } is a set of classes, where each class ci refers to a set of real world objects (class instances), (2) S ={s1 , s2 , . . . , sn } is a set of slots, where each slot si could refer to: a. a property of a class: a value of a simple type such as Integer, String or Date b. a binary typed role: the representation of a relation between classes. ACM Computing Surveys, Vol. 39, No. 4, Article 10, Publication date: October 2007. 3. Ontology Visualization MethodsA Survey10:3(3) isa ={isa1 , isa2 , . . . , isa p } is a set of inheritance relationships dened between classes. Inheritance relationships carry subset semantics and dene a partial order over classes, organizing classes into one or more tree structures. In order to accommodate the individual instances, this denition can be extended with a fourth element I = {i1 , i2 , . . . , iq }, where each iw is an instance of some class cx C. The instance includes a concrete value for every slot s y associated with cx or its ancestors (as dened by the isa set). 3. RELATED WORKThere are several works that review visualization techniques. They are not focused on ontologies, but attempt a more holistic view of techniques for visualizing many different types of data or documents. In Keim [2002], for example, apart from the categorization according to the type of data they support (e.g., text documents, images, processes, le system objects), techniques are divided into graphs, landscapes, dense pixel displays, and packed displays, from the visualization point of view, and in interactive projection, ltering, zooming, distortion, linking, and brushing from the interaction and distortion point of view. Young [1996] focuses mostly on 3D and distinguishes three general categories: mappings from the data domain to the visualization space (surface plots, cityscapes, etc.), information presentation techniques (perspective walls, cone trees, etc. and dynamic information visualization techniques (sh-eye views, self organizing graphs, etc.). The Shneiderman [1996] framework categorizes visualization methods based on two criteria, the data-type of the objects to be represented in the interface (linear, planar, volumetric, temporal, multidimensional, tree, network, workspace) and the task typology (overview, zoom, lter, details-on-demand, relate, history, extract). In another survey for 3D visualizations [Wiss and Carr, 1998] methods are examined from a cognitive point of view. Attention, abstraction and affordances are the cognitive aspects examined. Furthermore, designs are distinguished in node-link style designs (Cone Tree, Hyperbolic Space, etc.), Raised Surface Designs (Perspective Wall, Document Lens, etc.), Information Landscapes (FSN, Bead, Web Forager), and other designs (Web Book, Information Cube, etc.). In Herman et al. [2000], graph visualization techniques are presented and categorized from the graph drawing point of view. The Tao et al [2004] review approaches the issue of visualization from the point of view of Bioinformatics, including techniques for the presentation of the GO ontology [Gene Ontology Consortium www.go.org]. As there exist a number of ontology visualizations that are being used either in the context of ontology management tools or as information retrieval aids in applications that employ ontologies, some information on ontology visualization may be found in the ontology management tool surveys that can be retrieved from the Prot g Web e e pages [Prot g Project http://protege: Stanford.edu]. Ernst and Storey [2003] present e e the preliminary results of a survey using questionnaires related to ontology editing tools and ontology visualization. However, up to this point, there are not many comparative evaluations concerning the effectiveness of ontology visualization methods for different tasks and with different user groups. One example of such an evaluation focused on ontology visualization evaluation in the context of a historical archive is Katifori et al. [2006a]. Its results have been taken into account for the discussion sections. Other evaluations like Kobsa [2004], which is focused on the presentation of hierarchies in le browsers, and Wiss et al. [1998], which evaluates three 3D visualization methods, have also been taken into account. ACM Computing Surveys, Vol. 39, No. 4, Article 10, Publication date: October 2007. 4. 10:4A. Katifori et al. Table I. Equivalence of Document or File Categorization and Ontology Features File system objects Categorized documents Ontology Folder Category Entity (class or instance) Folder/subfolder relationship Category/subcategory relationship isa-relationship Tree view Categorization Taxonomy File Document Instance File properties Document properties SlotsThis article is an attempt to summarize existing literature related to ontology visualization, provide comprehensive cataloguing of existing method characteristics as well as record their strong points and weaknesses in relation with user tasks. 4. VISUALIZATION TECHNIQUES GROUPINGThe visualization techniques1 presented in the following sections were either specically created to display ontologies or were designed for other uses related to a tree or graph representation; for example for the visualization of a le system or a document categorization. Methods not created specically for ontologies have been included because the focus of this work is not the presentation of all existing ontology management tools, but rather of existing ontology visualizations. To this end, selected visualization techniques from relevant areas could provide ideas and insight into the research on ontology visualization. However, methods designed for other purposes probably need some modications in order to be used for the visualization of ontologies. For a method to be eligible for the visualization of an ontology, it has to support the presentation of ontology ingredients; classes (or entity types), relations, instances, and properties (or slots). For example, a straightforward equivalence among le system objects, categorized documents, and ontologies is illustrated in the following table. The methods can be grouped according to different characteristics of the presentation, interaction technique, functionality supported, or visualization dimensions. For the needs of this survey the methods were grouped in the following categories, representing their visualization type: 1. 2. 3. 4. 5. 6.Indented list, Nodelink and tree, Zoomable, Space-lling, Focus + context or distortion, 3D Information landscapes.Methods grouped in one of these categories may have elements of the other categories, for example, some space-lling techniques may also be zoomable. In these cases the predominant functionality features have been used for the categorization of the method. The effects of possible additional features on the performance of the visualization is presented in the respective discussion section. This grouping was chosen as a starting point because each of these general categories of visualizations has characteristics that lead to different advantages and weak points. There is a need to investigate how those relate to the special requirements of an ontology visualization tool in relation to the tasks a user would like to perform with an ontology visualization tool. 1 Visualizationmethods published until July 2006 have been considered.ACM Computing Surveys, Vol. 39, No. 4, Article 10, Publication date: October 2007. 5. Ontology Visualization MethodsA Survey10:5The methods grouped in these six general categories were further categorized according to the number of space dimensions they employ: 2D or 3D. 2D methods use the screen space as a plane and do not use any notion of depth. 3D methods exploit the third dimension either to create visualizations that are closer to real world metaphors or to improve usage of space and/or usability. More specically, these methods allow the user to manipulaterotate and move3D objects and/or to navigate inside the 3D space. 2 1/2D is a term applied to 2D visualizations that use a perspective view in order to create a sense of 3D without allowing movement or manipulation in the third dimension. Met...